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TOMOBOX


AI: poised to be the competitive edge


Fierce competition for player acquisition and growing churn has operators vying for new solutions, says Tomobox CEO, David Sachs.


I


n the new era of data, it’s no wonder that the market spoils data related companies like Apple, Facebook, Google and Amazon with mind boggling market valuations. Hence, the new kingpin sifting through those huge amounts of data is Artificial Intelligence.


 of computer science, is a set of algorithms that can predict an outcome based on historical data. While traditionally computing power was  abundance of cloud services affordable to even the most fragile upstarts has spurred an industry 


Demand is meeting supply where data-driven  improve their competitive edge. Online betting and iGaming have always been on the forefront of analytics and number crunching. Now,  a wide range of challenges ranging from player churn to fraud detection and even gaming compliance.


As competition for each and every player heats up, nowhere has player data analysis been so viable as for churn purposes. Fortunately, the abundance of data presents a compelling opportunity for operators, potentially helping to  patterns associated with player frustration that lead to churn.


Unfortunately, traditional analytical tools are limited to pre-coded decision trees, aimed at  of ever-changing data. Operators have done a great job with tracking the player journey  out on the cognitive side of players that is   


HOW CAN MACHINE LEARNING HELP IDENTIFY PLAYER ANXIETY?  


algorithms that can identify patterns within data. Machine learning is at its best when working through unstructured data; e.g. analyzing player frustration by sifting through player in-game text interactions and customer support chats that come in many languages and different variations. Player conversations reveal cognitive signals about players intent and thoughts about the game they play.


There are two approaches to modern   conversations are manually marked as potentially belonging to frustrated players and later used as training data for the machine learning code. As seen in the chart, the machine training results in two groups of players. Those above the hyper plane are marked as potential frustrated players with high probability. Those under the hyper plane are most probably not frustrated with the game and casino. The machine then generates a model that has a very good chance of identifying frustrated players that are highly probable to churn and might be moving on to a competing online casino. However, manual annotation of data for    learning, which requires data but does not require the training data to be pre-tagged.


Unsupervised classification, also known as clustering, has a tremendous value since it can point to anomalies that are very useful in identifying patterns and schemes.  adjust as data changes with time. For example, when detecting language that might point to players who dislike the dealer, sentences uttered by gamers such as: “Why are you touching the wheel, this casino  again”, are very different in nature but the  complete dislike of the game experience. Surprisingly enough, machines have become very astute in identifying these patterns.


Add to that the fact that these player- related insights, as presented by the machine, can be had in milliseconds not  make sure players are attended to in real time. Not after 24 hours, once they are long gone, but immediately.


Companies like Tomobox are working feverishly to adapt their machine learning algorithms to the online gaming industry to help humans assure that players have a much more positive experience. Other companies    better understand the world around us.  present a huge opportunity for operators to understand player behaviour and intent, address player frustration in real time and extend active playing time.


We’re facing a very powerful tool to make sure players are attended to in real time


Supervised and Unsupervised machine learning, each have their own advantages that data scientists exploit


CIO FEBRUARY 2019 75


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